Token Classification
Transformers
Safetensors
English
modernbert
named-entity-recognition
biomedical-nlp
leukemia
hematology
cancer
clinical-medicine
cl
Instructions to use OpenMed/OpenMed-NER-BloodCancerDetect-ModernMed-395M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-BloodCancerDetect-ModernMed-395M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-BloodCancerDetect-ModernMed-395M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-ModernMed-395M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-ModernMed-395M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-BloodCancerDetect-ModernMed-395M
1016465 verified - Xet hash:
- e719260ae6c08027f07f70592503bef42ee6c0be3c7eb29fff5449a6085ccb6f
- Size of remote file:
- 792 MB
- SHA256:
- f4af65759a2b357eb054bac805c04686f58c1075b677309f3f8ffae304d4490b
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